Optical Character Recognition (OCR)

Optical Character Recognition or Optical Character Reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo, license plates in cars...) or from subtitle text superimposed on an image (for example: from a television broadcast)

Papers

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Showing 881–890 of 1209 papers

Title	Date	Tasks	Status
Evaluating Usage of Images for App Classification	Dec 16, 2019	ClassificationGeneral Classification	—Unverified
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts	Dec 15, 2019	DiversityInstance Segmentation	CodeCode Available
Visual Summarization of Scholarly Videos using Word Embeddings and Keyphrase Extraction	Nov 25, 2019	Keyphrase ExtractionOptical Character Recognition	—Unverified
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images	Nov 25, 2019	Image Super-ResolutionOptical Character Recognition (OCR)	—Unverified
Handwritten and Machine printed OCR for Geez Numbers Using Artificial Neural Network	Nov 15, 2019	Image RetrievalOptical Character Recognition (OCR)	—Unverified
Character Keypoint-based Homography Estimation in Scanned Documents for Efficient Information Extraction	Nov 14, 2019	Homography EstimationOptical Character Recognition	—Unverified
Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks	Nov 13, 2019	Fine-Grained Vehicle ClassificationLicense Plate Detection	CodeCode Available
Recognition of Images of Korean Characters Using Embedded Networks	Nov 11, 2019	Optical Character Recognition (OCR)	—Unverified
Improving Long Handwritten Text Line Recognition with Convolutional Multi-way Associative Memory	Nov 5, 2019	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
VASTA: A Vision and Language-assisted Smartphone Task Automation System	Nov 4, 2019	ClusteringNatural Language Understanding	—Unverified

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All datasets Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study VideoDB's OCR Benchmark Public Collection FSNS - Test I2L-140K SUT im2latex-100k

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DTrOCR	Accuracy (%)	89.6	—	Unverified
2	DTrOCR 105M	Accuracy (%)	89.6	—	Unverified
3	MaskOCR-L	Accuracy (%)	82.6	—	Unverified
4	TransOCR	Accuracy (%)	72.8	—	Unverified
5	SRN	Accuracy (%)	65	—	Unverified
6	MORAN	Accuracy (%)	64.3	—	Unverified
7	SEED	Accuracy (%)	61.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GPT-4o	Average Accuracy	76.22	—	Unverified
2	Gemini-1.5 Pro	Average Accuracy	76.13	—	Unverified
3	Claude-3 Sonnet	Average Accuracy	67.71	—	Unverified
4	RapidOCR	Average Accuracy	56.98	—	Unverified
5	EasyOCR	Average Accuracy	49.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	STREET	Sequence error	27.54	—	Unverified
2	SEE	Sequence error	22	—	Unverified
3	AttentionOCR_Inception-resnet-v2_Location	Sequence error	15.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	I2L-NOPOOL	BLEU	89.09	—	Unverified
2	I2L-STRIPS	BLEU	89	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Tesseract	Character Error Rate (CER)	0.08	—	Unverified
2	EasyOCR	Character Error Rate (CER)	0.07	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	I2L-STRIPS	BLEU	88.86	—	Unverified